
"""simple docstring"""


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
      from PIL import Image
else:



      class 	__a	:






             @staticmethod
             def 						_SCREAMING_SNAKE_CASE							(	*UpperCAmelCase_			:				Any							,   **UpperCAmelCase_			:				Optional[Any]      )->	Union[str, Any]:







                    """simple docstring"""
                    pass



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				str:







       """simple docstring"""
       UpperCamelCase       			=		hashlib.mda(image.tobytes()					)
       return m.hexdigest()[:10]



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Dict:







       """simple docstring"""
       UpperCamelCase       			=		np.array(UpperCAmelCase_					)
       UpperCamelCase       			=		npimg.shape
       return {"hash": hashimage(UpperCAmelCase_					), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 	__a	(   unittest.TestCase						):
       UpperCamelCase_		:       Optional[int]				=      dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if MODEL_FOR_MASK_GENERATION_MAPPING else [])						)
       UpperCamelCase_		:       Optional[Any]				=      dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()						) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])						)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				Dict      )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		MaskGenerationPipeline(model=UpperCAmelCase_							,   image_processor=UpperCAmelCase_      )
              return image_segmenter, [
                  "./tests/fixtures/tests_samples/COCO/000000039769.png",
                  "./tests/fixtures/tests_samples/COCO/000000039769.png",
              ]






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				int							,   UpperCAmelCase_			:				Optional[int]      )->	Tuple:







              """simple docstring"""
              pass






       @require_tf
       @unittest.skip("Image segmentation not implemented in TF"      )
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              pass






       @slow
       @require_torch
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple      )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		pipeline("mask-generation"							,   model="facebook/sam-vit-huge"      )

              UpperCamelCase       			=		image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"							,   points_per_batch=256      )

              # Shortening by hashing
              UpperCamelCase       			=		[]
              for i, o in enumerate(outputs["masks"]      ):
                     new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase_      ), "scores": outputs["scores"][i]}]

              # fmt: off
              self.assertEqual(
                  nested_simplify(UpperCAmelCase_							,   decimals=4      )							,   [
                      {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                      {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.021},
                      {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                      {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                      {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
                      {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.9967},
                      {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.993},
                      {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.9909},
                      {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.9879},
                      {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.9834},
                      {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.9716},
                      {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.9612},
                      {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.9599},
                      {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.9552},
                      {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.9532},
                      {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.9516},
                      {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.9499},
                      {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.9483},
                      {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.9464},
                      {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.943},
                      {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.943},
                      {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.9408},
                      {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.9335},
                      {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.9326},
                      {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.9262},
                      {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.8999},
                      {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.8986},
                      {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.8984},
                      {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.8873},
                      {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.8871}
                  ]							,   )
              # fmt: on






       @require_torch
       @slow
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		"facebook/sam-vit-huge"
              UpperCamelCase       			=		pipeline("mask-generation"							,   model=UpperCAmelCase_      )

              UpperCamelCase       			=		image_segmenter(
                  "http://images.cocodataset.org/val2017/000000039769.jpg"							,   pred_iou_thresh=1							,   points_per_batch=256      )

              # Shortening by hashing
              UpperCamelCase       			=		[]
              for i, o in enumerate(outputs["masks"]      ):
                     new_outupt += [{"mask": mask_to_test_readable(UpperCAmelCase_      ), "scores": outputs["scores"][i]}]

              self.assertEqual(
                  nested_simplify(UpperCAmelCase_							,   decimals=4      )							,   [
                      {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.0444},
                      {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0210},
                      {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.0167},
                      {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.0132},
                      {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.0053},
                  ]							,   )







"""simple docstring"""


import numpy as np



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_ = 1E-12		,   UpperCAmelCase_ = 1_00		,   )->				tuple[float, np.ndarray]:







       """simple docstring"""
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[1]
       # Ensure proper dimensionality.
       assert np.shape(UpperCAmelCase_					)[0] == np.shape(UpperCAmelCase_					)[0]
       # Ensure inputs are either both complex or both real
       assert np.iscomplexobj(UpperCAmelCase_					) == np.iscomplexobj(UpperCAmelCase_					)
       UpperCamelCase       			=		np.iscomplexobj(UpperCAmelCase_					)
       if is_complex:
              # Ensure complex input_matrix is Hermitian
              assert np.array_equal(UpperCAmelCase_		,   input_matrix.conj().T					)

       # Set convergence to False. Will define convergence when we exceed max_iterations
       # or when we have small changes from one iteration to next.

       UpperCamelCase       			=		False
       UpperCamelCase       			=		0
       UpperCamelCase       			=		0
       UpperCamelCase       			=		1E12

       while not convergence:
              # Multiple matrix by the vector.
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)
              # Normalize the resulting output vector.
              UpperCamelCase       			=		w / np.linalg.norm(UpperCAmelCase_					)
              # Find rayleigh quotient
              # (faster than usual b/c we know vector is normalized already)
              UpperCamelCase       			=		vector.conj().T if is_complex else vector.T
              UpperCamelCase       			=		np.dot(UpperCAmelCase_		,   np.dot(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              # Check convergence.
              UpperCamelCase       			=		np.abs(lambda_ - lambda_previous					) / lambda_
              iterations += 1

              if error <= error_tol or iterations >= max_iterations:
                     UpperCamelCase       			=		True

              UpperCamelCase       			=		lambda_

       if is_complex:
              UpperCamelCase       			=		np.real(lambda_					)

       return lambda_, vector



def 						lowerCamelCase__      (				)->				None:







       """simple docstring"""
       UpperCamelCase       			=		np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
       UpperCamelCase       			=		np.array([41, 4, 20]					)
       UpperCamelCase       			=		real_input_matrix.astype(np.complexaaa					)
       UpperCamelCase       			=		np.triu(1j * complex_input_matrix		,   1					)
       complex_input_matrix += imag_matrix
       complex_input_matrix += -1 * imag_matrix.T
       UpperCamelCase       			=		np.array([41, 4, 20]					).astype(np.complexaaa					)

       for problem_type in ["real", "complex"]:
              if problem_type == "real":
                     UpperCamelCase       			=		real_input_matrix
                     UpperCamelCase       			=		real_vector
              elif problem_type == "complex":
                     UpperCamelCase       			=		complex_input_matrix
                     UpperCamelCase       			=		complex_vector

              # Our implementation.
              UpperCamelCase			,							UpperCamelCase       			=		power_iteration(UpperCAmelCase_		,   UpperCAmelCase_					)

              # Numpy implementation.

              # Get eigenvalues and eigenvectors using built-in numpy
              # eigh (eigh used for symmetric or hermetian matrices).
              UpperCamelCase			,							UpperCamelCase       			=		np.linalg.eigh(UpperCAmelCase_					)
              # Last eigenvalue is the maximum one.
              UpperCamelCase       			=		eigen_values[-1]
              # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
              UpperCamelCase       			=		eigen_vectors[:, -1]

              # Check our implementation and numpy gives close answers.
              assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
              # Take absolute values element wise of each eigenvector.
              # as they are only unique to a minus sign.
              assert np.linalg.norm(np.abs(UpperCAmelCase_					) - np.abs(UpperCAmelCase_					)					) <= 1E-6


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      test_power_iteration()





